Course details

FacultyHealth Sciences
Education levelPostgraduate / Master of Science
Course codeE1Semester2
Course titleAdvanced Statistics
Independent teaching activitiesHours per weekECTS
CoursetypeGeneral setting course, skills development
Prerequisite coursesNone
Teaching and assessment languageEnglish

Learning outcomes


Upon completion of the course, students will be able to understand the importance of the application of regression models in medical research. They will be able to model the relationship between a dependent variable with one or more explanatory (independent) variables. Students will be capable to use, evaluate and interpret results taking into account possible confounding and effect modification as well as clinical outcomes.
An additional objective of this course is the understanding of biostatistics methodology through practical guidance and use of R program.


Upon completion of the course, graduate students will be familiar with:

  • Simple and multiple linear models applied to medical data (linear, logistic, Cox)
  • Assumptions and conditions that should be met for the application of these models
  • The goodness of fit test to evaluate the performance of the models
  • The necessary conditions that must exist in order to qualify as one or more variables as confounding in a relationship of outcome and exposure
  • The concept of effect modification, and how it differs from confounding
  • The usefulness of multiple regression techniques to analyze the relationship of an exposure to a predictive variable in the possible presence of confounding factors by using adjusted analysis

The course participants upon completion will be able to:

  • Have a methodology in order to build appropriate multivariable linear models
  • Investigate the existence of confounding or effect modification in medical research and manage them appropriately
  • Interpretation of the results obtained from the corresponding models
  • Using the fast-growing and evolving R project as a tool for statistical analysis and the creation of elegant graphs

Course contents

  1. Data investigation and editing, guidance for constructing graphs using R program (practice R)
  2. Linear relationship between two quantitative variables (Scatter plots, Pearson’s & Spearman’s correlation) (practice R)
  3. Theory of Linear Regression Models and their importance in medical research and practice
  4. Confounding and effect modification
  5. Simple and multiple linear regression analysis and applications in medical data (in practice R)
  6. Simple and multiple logistic regression analysis and applications in medical data (in practice R)
  7. Simple and multiple Cox regression and applications in medical data (in practice R)

Teaching and learning methods – evaluation

Teaching methodsFace to face
Distance learning
Use of information and
communication technologies (ICT)

  • Use of ICT in Teaching- Moodle Virtual learning environment (VLE)
    (asynchronous learning, wikis, Online Discussion Fora, Educational Portfolio, assignment submission, assessment process)

  • Use of ICT in Communication with students
    (email, instant messaging via Moodle)

Module structureWork Hours per SemesterActivity
Lectures 30
Exercises (Quiz) 5
Exercises (Wikis) 5
Exercises (Online discussion fora) 10
Exercises (Study relevant papers) 20
Essay background work40
Essay writing15
Overall work for the course125
Assessment Methods
  • Written assignment, in English, approximately 2,500 words long, to be submitted by each student at the end of the course

  • Assessment of knowledge at the beginning and the end of the course with short-answer questions and essays development

  • Weekly quizes, with multiple choice questions

  • Assessment based on comments submitted by each student in online discussion fora

Recommended Bibliography

  1. Aho, Ken A. Foundational and applied statistics for biologists using R. CRC Press, 2013.
  2. Bland, Martin. An introduction to medical statistics. 3rd Edition. Oxford University Press, 2000.
  3. Crawley, Michael J. Statistics: an introduction using R, 2nd Edition. John Wiley & Sons, 2014.
  4. MacFarland, Thomas W. Introduction to Data Analysis and Graphical Presentation in Biostatistics with R. Springer, 2014.
  5. Daniel, Wayne W., and Chad L. Cross. Biostatistics: A Foundation for Analysis in the Health Sciences: A Foundation for Analysis in the Health Sciences. Wiley Global Education, 2012.
  6. Logan M. Biostatistical Design and Analysis Using R: A Practical Guide. Wiley-Blackwell, 2010.
  7. Aviva Petrie, Caroline Sabin. Medical Statistics at a Glance, 3rd Wiley  2009.
  8. David G. Kleinbaum, Mitchel Klein. Survival Analysis: A self-learning text. 3rd Edition. Springer 2012.
  9. David G. Kleinbaum. Logistic Regression: A self-learning text. 3rd Edition. Springer 2010.